AI monetization, valuation doubt and the power of storytelling
Miguel McKelvey, co-founder of WeWork and founder of Unbound, says WeWork’s valuation made sense because it solved tangible, real-world problems. He argues today’s AI monetization is still unclear, making AI company valuations confusing.
McKelvey draws a parallel to Uber/Lyft-style businesses: physical, measurable outcomes supported WeWork’s valuation. By contrast, many AI applications are powerful but lack clear revenue models, so it is hard to judge “how they will be evaluated.”
He also highlights marketing mechanics for premium products: brands must communicate value clearly, repeat their “why,” and use storytelling to drive consumer engagement. Public visibility can boost recognition (he cites a local-restaurant example of giving products to servers so people ask where they’re from).
For small businesses, he notes a growth ceiling—Unbound reported staying around $200,000 in revenue for three years and falling about 7% year-over-year in a tougher recent year.
On digital strategy, McKelvey recommends optimizing content for both SEO and AI-driven discovery, including creating web pages for many search scenarios and distributing answers across formats like TikTok and YouTube. He suggests “AI scrapers” are emerging as the new SEO.
Separately, he claims unmanaged workplace grief has a major fiscal impact on employers (citing ~$75B+ per year), implying an opportunity for grief-related services.
Overall, the interview frames AI monetization and valuation uncertainty like prior tech booms—where clear business models and branding execution ultimately matter.
Neutral
This story is about tech business models and marketing/SEO strategy, not about crypto networks, tokens, regulation, or a protocol-level catalyst. The only potentially relevant angle is that “AI monetization uncertainty” can feed broader risk appetite around AI-linked equity/tech narratives; however, the article provides no direct cryptocurrency, token economics, or market mechanism tied to trading.
In crypto history, macro or tech-sector sentiment often moves markets indirectly (e.g., during past AI hype cycles), but those effects typically depend on concrete outcomes—earnings, funding rounds, regulatory changes, or on-chain adoption. Here, the signals are qualitative: valuations are confusing when revenue models are unclear, and distribution (SEO/AI discovery) plus clear branding can matter.
Short-term, traders are unlikely to reprice crypto based solely on an interview. Long-term, if the market collectively believes AI products will not monetize cleanly, sentiment toward high-beta tech narratives could soften, but that’s still an indirect and diffuse influence. Therefore, the expected impact on crypto stability is neutral.